Cortical gene expression correlates of temporal lobe epileptogenicity

Leukocyte differential gene expression prognostic value for high versus low seizure frequency in temporal lobe epilepsy

BACKGROUND

This study was performed to test the hypothesis that systemic leukocyte gene expression has prognostic value differentiating low from high seizure frequency refractory temporal lobe epilepsy (TLE).

METHODS

A consecutive series of patients with refractory temporal lobe epilepsy was studied. Based on a median baseline seizure frequency of 2.0 seizures per month, low versus high seizure frequency was defined as ≤ 2 seizures/month and > 2 seizures/month, respectively. Systemic leukocyte gene expression was analyzed for prognostic value for TLE seizure frequency. All differentially expressed genes were analyzed, with Ingenuity® Pathway Analysis (IPA®) and Reactome, to identify leukocyte gene expression and biological pathways with prognostic value for seizure frequency.

RESULTS

There were ten males and six females with a mean age of 39.4 years (range: 16 to 62 years, standard error of mean: 3.6 years). There were five patients in the high and eleven patients in the low seizure frequency cohorts, respectively. Based on a threshold of twofold change (p < 0.001, FC > 2.0, FDR < 0.05) and expression within at least two pathways from both Reactome and Ingenuity® Pathway Analysis (IPA®), 13 differentially expressed leukocyte genes were identified which were all over-expressed in the low when compared to the high seizure frequency groups, including NCF2, HMOX1, RHOB, FCGR2A, PRKCD, RAC2, TLR1, CHP1, TNFRSF1A, IFNGR1, LYN, MYD88, and CASP1. Similar analysis identified four differentially expressed genes which were all over-expressed in the high when compared to the low seizure frequency groups, including AK1, F2R, GNB5, and TYMS.

CONCLUSIONS

Low and high seizure frequency TLE are predicted by the respective upregulation and downregulation of specific leukocyte genes involved in canonical pathways of neuroinflammation, oxidative stress and lipid peroxidation, GABA (γ-aminobutyric acid) inhibition, and AMPA and NMDA receptor signaling. Furthermore, high seizure frequency-TLE is distinguished prognostically from low seizure frequency-TLE by differentially increased specific leukocyte gene expression involved in GABA inhibition and NMDA receptor signaling. High and low seizure frequency patients appear to represent two mechanistically different forms of temporal lobe epilepsy based on leukocyte gene expression.

Biomarkers for epileptogenesis and its treatment

There are no pharmacological interventions to prevent the development of epilepsy, although many promising compounds have been identified in the animal laboratory. Clinical trials to validate their effectiveness, however, would currently be prohibitively expensive due to the large subject population and duration of follow-up necessary. There is, therefore, the need to identify biomarkers of epileptogenesis that could identify patients at high risk for epilepsy following a potential epileptogenic insult to enrich the subject population, as well as biomarkers that could determine the effectiveness of therapeutic intervention without the need to wait for seizures to occur. Putative biomarkers under investigation for epileptogenesis and its treatment include genetic, molecular, cellular, imaging, and electrophysiological measures that might reliably predict the development or progression of an epileptic condition, the effects of antiepileptogenic treatment, or cure after surgery. To be clinically useful for most purposes, ideal biomarkers should be noninvasive, and it is anticipated that a profile of multiple biomarkers will likely be required. Ongoing animal research involves a number of experimental models of epileptogenesis, with traumatic brain injury, offering the best potential for translational clinical investigations. Collaborative and multicenter research efforts by multidisciplinary teams of basic and clinical neuroscientists with access to robust, well-defined animal models, extensive patient populations, standardized protocols, and cutting-edge analytical methodologies are likely to be most successful. Such biomarker research should also provide insights into fundamental neuronal mechanisms of epileptogenesis suggesting novel targets for antiepileptogenic treatments. This article is part of the special issue entitled 'New Epilepsy Therapies for the 21st Century - From Antiseizure Drugs to Prevention, Modification and Cure of Epilepsy'.

Leukocyte expression profiles reveal gene sets with prognostic value for seizure-free outcome following stereotactic laser amygdalohippocampotomy

Among patients with intractable epilepsy, the most commonly performed surgical procedure is craniotomy for amygdalohippocampectomy (AH). Stereotactic laser amygdalohippocampotomy (SLAH) has also been recently employed as a minimally invasive treatment for intractable temporal lobe epilepsy (TLE). Among patients treated with AH and SLAH approximately 65% and 54% of patients become seizure-free, respectively. Therefore, selection criteria for surgical candidates with improved prognostic value for post-operative seizure-free outcome are greatly needed. In this study, we perform RNA sequencing (RNA-Seq) on whole blood leukocyte samples taken from 16 patients with intractable TLE prior to SLAH to test the hypothesis that pre-operative leukocyte RNA expression profiles are prognostic for post-operative seizure outcome. Multidimensional scaling analysis of the RNA expression data indicated separate clustering of patients with seizure free (SF) and non-seizure-free (NSF) outcomes. Differential expression (DE) analysis performed on SF versus NSF groups revealed 24 significantly differentially expressed genes (≥2.0-fold change, p-value < 0.05, FDR <0.05). Network and pathway analyses identified differential activation of pathways involved in lipid metabolism, morphology of oligodendrocytes, inflammatory response, and development of astrocytes. These results suggest that pre-operative leukocyte expression profiles have prognostic value for seizure outcome following SLAH.

Hepatocyte Growth Factor Activator: A Proteinase Linking Tissue Injury with Repair

Hepatocyte growth factor (HGF) promotes pleiotropic signaling through its specific receptor tyrosine kinase, MET. As such, it has important roles in the regeneration of injured tissues. Since HGF is produced mainly by mesenchymal cells and MET is expressed in most epithelial, endothelial and somatic stem cells, HGF functions as a typical paracrine growth factor. HGF is secreted as an inactive precursor (proHGF) and requires proteolytic activation to initiate HGF-induced MET signaling. HGF activator (HGFAC) is a serum activator of proHGF and produces robust HGF activities in injured tissues. HGFAC is a coagulation factor XII-like serine endopeptidase that circulates in the plasma as a zymogen (proHGFAC). Thrombin, kallikrein-related peptidase (KLK)-4 or KLK-5 efficiently activates proHGFAC. The activated HGFAC cleaves proHGF at Arg494-Val495, resulting in the formation of the active disulfide-linked heterodimer HGF. Macrophage stimulating protein, a ligand of RON, is also activated by HGFAC in vivo. Although HGFAC functions primarily at the site of damaged tissue, a recent report has suggested that activated HGFAC relays a signal to stem cells in non-injured tissues via proHGF activation in the stem cell niche. This review focuses on current knowledge regarding HGFAC-mediated proHGF activation and its roles in tissue regeneration and repair.

Epilepsy biomarkers - Toward etiology and pathology specificity

A biomarker is a characteristic that is measured as an indicator of normal biologic processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions. Biomarker modalities include molecular, histologic, radiographic, or physiologic characteristics. In 2015, the FDA-NIH Joint Leadership Council developed the BEST Resource (Biomarkers, EndpointS, and other Tools) to improve the understanding and use of biomarker terminology in biomedical research, clinical practice, and medical product development. The BEST biomarker categories include: (a) susceptibility/risk biomarkers, (b) diagnostic biomarkers, (c) monitoring biomarkers, (d) prognostic biomarkers, (e) predictive biomarkers, (f) pharmacodynamic/response biomarkers, and (g) safety biomarkers. Here we review 30 epilepsy biomarker studies that have identified (a) diagnostic biomarkers for epilepsy, epileptogenesis, epileptogenicity, drug-refractoriness, and status epilepticus - some of the epileptogenesis and epileptogenicity biomarkers can also be considered prognostic biomarkers for the development of epilepsy in subjects with a given brain insult, (b) predictive biomarkers for epilepsy surgery outcome, and (c) a response biomarker for therapy outcome. The biomarker modalities include plasma/serum/exosomal and cerebrospinal fluid molecular biomarkers, brain tissue molecular biomarkers, imaging biomarkers, electrophysiologic biomarkers, and behavioral/cognitive biomarkers. Both single and combinatory biomarkers have been described. Most of the reviewed biomarkers have an area under the curve >0.800 in receiver operating characteristics analysis, suggesting high sensitivity and specificity. As discussed in this review, we are in the early phase of the learning curve in epilepsy biomarker discovery. Many of the seven biomarker categories lack epilepsy-related biomarkers. There is a need for epilepsy biomarker discovery using proper, statistically powered study designs with validation cohorts, and the development and use of novel analytical methods. A strategic roadmap to discuss the research priorities in epilepsy biomarker discovery, regulatory issues, and optimization of the use of resources, similar to those devised in the cancer and Alzheimer's disease research areas, is also needed.

Identification of potential drug targets for treatment of refractory epilepsy using network pharmacology

Epilepsy is the fourth most common neurological disease after migraine, stroke, and Alzheimer's disease. Approximately one-third of all epilepsy cases are refractory to the existing anticonvulsants. Thus, there is an unmet need for newer antiepileptic drugs (AEDs) to manage refractory epilepsy (RE). Discovery of novel AEDs for the treatment of RE further retards for want of potential pharmacological targets, unavailable due to unclear etiology of this disease. In this regard, network pharmacology as an area of bioinformatics is gaining popularity. It combines the methods of network biology and polypharmacology, which makes it a promising approach for finding new molecular targets. This work is aimed at discovering new pharmacological targets for the treatment of RE using network pharmacology methods. In the framework of our study, the genes associated with the development of RE were selected based on analysis of available data. The methods of network pharmacology were used to select 83 potential pharmacological targets linked to the selected genes. Then, 10 most promising targets were chosen based on analysis of published data. All selected target proteins participate in biological processes, which are considered to play a key role in the development of RE. For 9 of 10 selected targets, the potential associations with different kinds of epilepsy have been recently mentioned in the literature published, which gives additional evidence that the approach applied is rather promising.